# Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm

## TL;DR

This paper explores EEG-based brain signal classification using tensor machine learning to identify digital biomarkers for dementia stages, aiming to support early diagnosis and intervention in aging populations.

## Contribution

It introduces a tensor-based machine learning approach for classifying ERPs related to dementia, advancing AI methods for early cognitive impairment detection.

## Key findings

- Tensor machine learning outperforms other methods in ERP classification.
- EEG signals show distinguishable patterns for high and low task-load stimuli.
- Potential for developing digital biomarkers for dementia diagnostics.

## Abstract

Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published report by The World Health Organization (WHO) estimates that about 47 million people are suffering from dementia-related neurocognitive declines worldwide. The number of dementia cases is predicted by 2050 to triple, which requires the creation of an AI-based technology application to support interventions with early screening for subsequent mental wellbeing checking as well as preservation with digital-pharma (the so-called beyond a pill) therapeutical approaches. We present an attempt and exploratory results of brain signal (EEG) classification to establish digital biomarkers for dementia stage elucidation. We discuss a comparison of various machine learning approaches for automatic event-related potentials (ERPs) classification of a high and low task-load sound stimulus recognition. These ERPs are similar to those in dementia. The proposed winning method using tensor-based machine learning in a deep fully connected neural network setting is a step forward to develop AI-based approaches for a subsequent application for subjective- and mild-cognitive impairment (SCI and MCI) diagnostics.